# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Main file for running audio experiments."""

from absl import app
from absl import flags
from absl import logging
from clu import platform
import jax
from ml_collections import config_flags
import tensorflow as tf
from autoregressive_diffusion.experiments.audio import train


flags.DEFINE_string('experiment_dir', None, 'Experiment output directory.')
flags.DEFINE_string('executable_name', None, 'Executable name.')
flags.DEFINE_string('work_unit_dir', None, 'Directory to store model data.')

config_flags.DEFINE_config_file(
    'config',
    None,
    'File path to the training hyperparameter configuration.',
    lock_config=False)

flags.mark_flags_as_required(['config', 'work_unit_dir'])

FLAGS = flags.FLAGS


def main(argv):
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  # Hide any GPUs form TensorFlow. Otherwise TF might reserve memory and make
  # it unavailable to JAX.
  tf.config.experimental.set_visible_devices([], 'GPU')

  logging.info('JAX process: %d / %d', jax.process_index(), jax.process_count())
  logging.info('JAX local devices: %r', jax.local_devices())

  # Add a note so that we can tell which task is which JAX host.
  # (Depending on the platform task 0 is not guaranteed to be host 0)
  platform.work_unit().set_task_status(f'process_index: {jax.process_index()}, '
                                       f'process_count: {jax.process_count()}')
  platform.work_unit().create_artifact(platform.ArtifactType.DIRECTORY,
                                       FLAGS.work_unit_dir, 'work_unit_dir')

  run_fn = getattr(train, FLAGS.executable_name)
  run_fn(FLAGS.config, FLAGS.work_unit_dir)


if __name__ == '__main__':
  app.run(main)
